This paper presents a novel navigation system for aiding a classic inertial navigation system with a Vehicle Dynamics Model in a quadrotor application. The navigation system is based on the previously presented Unified Model technique for optimal fusion of the two prediction models. The key point of the approach in this paper is that the required modeling and implementation effort is minimized by incorporating a translational dynamics model only. Even though no rotational vehicle dynamics are modeled, it is shown that the filter is able to estimate roll and pitch angles and even IMU biases with bounded errors. Indoor and outdoor flight experiments confirm that all navigation states except heading angle are significantly improved compared to the single systems. For the first time, this demonstrates in real flight experiments that model‐aided navigation allows estimating a full navigation solution for several minutes even without any aiding sensors. Copyright © 2014 Institute of Navigation.
Model‐aided navigation increases navigation accuracy by including a vehicle dynamics model into the filter structure. The commonly used Inertial Navigation System (INS) is hence supplemented by another prediction model for the system state. However, the standard Kalman filter only allows for a single system model to propagate the estimation. The main contribution of this paper is the improvement of an existing approach to estimation with two valid state prediction models. By unifying the models, computation time and state vector size are reduced. Furthermore, the question of how the models must be coupled to achieve optimality and preserve filter stability is addressed.In integrated aircraft navigation, an INS as well as a vehicle dynamics model are available. The presented method unifies these two models and shows superior computational performance compared to existing model‐aided navigation methods and among best results. Furthermore, it is easy to implement and easy to extend with aiding sensors. Copyright © 2013 Institute of Navigation.
This paper presents a laser-aided navigation system for Micro Aerial Vehicles. It is based on a Kalman filter so that GNSS measurements can be incorporated if available. For GNSS-denied areas, the Kalman filter also processes relative pose measurements extracted from laser data. A novel approach for laser-aided Kalman filter navigation is presented which allows using multiple reference scans simultaneously. Furthermore, an addition to avoid growth of the heading angle error is described.Because this Kalman filter based system is a relative navigation system, its position error grows with time. To avoid such an error growth in GNSS denied environments, the Kalman filter is augmented with loop closure detection. A technique is proposed to represent such information in a pose graph and to calculate an improved navigation solution including an error covariance based on covariance intersection. The successful operation of the presented system is validated in several experiments including real flight data, large loops and an outdoor-indoor-outdoor transition.
This paper focuses on the real time implementation of cooperative navigation aiding for a small unmanned aerial vehicle (UAV) based on vision systems on board unmanned ground vehicles (UGV). In urban environments the signal quality of global satellite navigation systems (GNSS) often is bad or signals are even lost, so that the navigation solution of a UAV is affected. Especially in such situations the UAV's geo-referenced position has to be known to ensure a safe guidance. A team of UGVs can overcome the problem of no GNSS position solution by the detection and tracking of the UAV in its on board images and the subsequent geo-localization. The determination of the UAV's geo-referenced position can be achieved with several algorithms, depending on the number of UGVs. To integrate these position measurements in the UAV's on board navigation computer the quality of the detection and geo-localization has to be estimated. All parts are implemented on embedded PC platforms. To allow real time usage on board the UGVs special algorithms have to be used for the most tasks, as image processing needs high computational power. The particular parts of this setup as well as the whole system are tested with experimental data.
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